Research Area: Computational Neuroscience

​machine learning, modeling, artificial intelligence, neural networks, memory, ​visual intelligence, audition, natural language processing, learning theory

Recurrent architecture enhances object recognition in brain and AI

by Sabbi Lall |

Your ability to recognize objects is remarkable. If you see a cup under unusual lighting or from unexpected directions, there’s a good chance that your brain will still compute that it is a cup. Such precise object recognition is one holy grail for AI developers, such as those improving self-driving car navigation. While modeling primate object recognition in the visual cortex has revolutionized artificial visual recognition systems, current deep learning systems are simplified, and fail to recognize some objects that are child’s play for primates such as humans. In findings published in Nature Neuroscience, McGovern Investigator James DiCarlo and colleagues have found evidence that feedback improves recognition of hard-to-recognize objects in the primate brain, and that adding feedback circuitry also improves the performance of artificial neural network systems used for vision applications.

Deep convolutional neural networks (DCNN) are currently the most successful models for accurately recognizing objects on a fast timescale (<100 ms) and have a general architecture inspired by the primate ventral visual stream, cortical regions that progressively build an accessible and refined representation of viewed objects. Most DCNNs are simple in comparison to the primate ventral stream however.

“For a long period of time, we were far from an model-based understanding. Thus our field got started on this quest by modeling visual recognition as a feedforward process,” explains senior author DiCarlo, who is also the head of MIT’s Department of Brain and Cognitive Sciences and Research Co-Leader in the Center for Brains, Minds, and Machines (CBMM). “However, we know there are recurrent anatomical connections in brain regions linked to object recognition.”

Think of feedforward DCNNs and the portion of the visual system that first attempts to capture objects as a subway line that runs forward through a series of stations. The extra, recurrent brain networks are instead like the streets above, interconnected and not unidirectional. Because it only takes about 200 ms for the brain to recognize an object quite accurately, it was unclear if these recurrent interconnections in the brain had any role at all in core object recognition. For example, perhaps those recurrent connections are only in place to keep the visual system in tune over long periods of time. For example, the return gutters of the streets help slowly clear it of water and trash, but are not strictly needed to quickly move people from one end of town to the other. DiCarlo, along with lead author and CBMM postdoc Kohitij Kar, set out to test whether a subtle role of recurrent operations in rapid visual object recognition was being overlooked.

Challenging recognition

The authors first needed to identify objects that are trivially decoded by the primate brain, but are challenging for artificial systems. Rather than trying to guess why deep learning was having problems recognizing an object (is it due to clutter in the image? a misleading shadow?), the authors took an unbiased approach that turned out to be critical.

Kar explained further that “we realized that AI-models actually don’t have problems with every image where an object is occluded or in clutter. Humans trying to guess why AI models were challenged turned out to be holding us back.”

Instead, the authors presented the deep learning system, as well as monkeys and humans, with images, homing in on “challenge images” where the primates could easily recognize the objects in those images, but a feed forward DCNN ran into problems. When they, and others, added appropriate recurrent processing to these DCNNs, object recognition in challenge images suddenly became a breeze.

Processing times

Kar used neural recording methods with very high spatial and temporal precision to whether these images were really so trivial for primates. Remarkably, they found that though challenge images had initially appeared to be child’s play to the human brain, they actually involve extra neural processing time (about additional 30 milliseconds), suggesting that recurrent loops operate in our brain too.

 “What the computer vision community has recently achieved by stacking more and more layers onto artificial neural networks, evolution has achieved through a brain architecture with recurrent connections.” — Kohitij Kar

Diane Beck, Professor of Psychology and Co-chair of the Intelligent Systems Theme at the Beckman Institute and not an author on the study, explained further. “Since entirely feed forward deep convolutional nets are now remarkably good at predicting primate brain activity, it raised questions about the role of feedback connections in the primate brain. This study shows that, yes, feedback connections are very likely playing a role in object recognition after all.”

What does this mean for a self-driving car? It shows that deep learning architectures involved in object recognition need recurrent components if they are to match the primate brain, and also indicates how to operationalize this procedure for the next generation of intelligent machines.

“Recurrent models offer predictions of neural activity and behavior over time,” says Kar. “We may now be able to model more involved tasks. Perhaps one day, the systems will not only recognize an object, such as a person, but also perform cognitive tasks that the human brain so easily manages, such as understanding the emotions of other people.”

This work was supported by the Office of Naval Research grant MURI-114407 (J.J.D.). Center for Brains, Minds, and Machines (CBMM) funded by NSF STC award CCF-1231216 (K.K.).

3Q: The interface between art and neuroscience

by Connie Blaszczyk | Center for Art, Science, and Technology |

CBMM postdoc Sarah Schwettman

Computational neuroscientist Sarah Schwettmann, who works in the Center for Brains, Minds, and Machines at the McGovern Institute, is one of three instructors behind the cross-disciplinary course 9.S52/9.S916 (Vision in Art and Neuroscience), which introduces students to core concepts in visual perception through the lenses of art and neuroscience.

Supported by a faculty grant from the Center for Art, Science and Technology at MIT (CAST) for the past two years, the class is led by Pawan Sinha, a professor of vision and computational neuroscience in the Department of Brain and Cognitive Sciences. They are joined in the course by Seth Riskin SM ’89, a light artist and the manager of the MIT Museum Studio and Compton Gallery, where the course is taught. Schwettman discussed the combination of art and science in an educational setting.

Q: How have the three of you approached this cross-disciplinary class in art and neuroscience?

A: Discussions around this intersection often consider what each field has to offer the other. We take a different approach, one I refer to as occupying the gap, or positioning ourselves between the two fields and asking what essential questions underlie them both. One question addresses the nature of the human relationship to the world. The course suggests one answer: This relationship is fundamentally creative, from the brain’s interpretation of incoming sensory data in perception, to the explicit construction of experiential worlds in art.

Neuroscience and art, therefore, each provide a set of tools for investigating different levels of the constructive process. Through neuroscience, we develop a specific understanding of the models of the world that the brain uses to make sense of incoming visual data. With articulation of those models, we can engineer types of inputs that interact with visual processing architecture in particularly exquisite ways, and do so reliably, giving artists a toolkit for remixing and modulating experience. In the studio component of the course, we experiment with this toolkit and collectively move it forward.

While designing the course, Pawan, Seth, and I found that we were each addressing a similar set of questions, the same that motivate the class, through our own research and practice. In parallel to computational vision research, Professor Sinha leads a humanitarian initiative called Project Prakash, which provides treatment to blind children in India and explores the development of vision following the restoration of sight. Where does structure in perception originate? As an artist in the MIT Museum Studio, Seth works with articulated light to sculpt structured visual worlds out of darkness. I also live on this interface where the brain meets the world — my research in the Department of Brain and Cognitive Sciences examines the neural basis of mental models for simulating physics. Linking our work in the course is an experiment in synthesis.

Q: What current research in vision, neuroscience, and art are being explored at MIT, and how does the class connect it to hands-on practice?

A: Our brains build a rich world of experience and expectation from limited and noisy sensory data with infinite potential interpretations. In perception research, we seek to discover how the brain finds more meaning in incoming data than is explained by the signal alone. Work being done at MIT around generative models addresses this, for instance in the labs of Josh Tenenbaum and Josh McDermott in the Department of Brain and Cognitive Sciences. Researchers present an ambiguous visual or auditory stimulus and by probing someone’s perceptual interpretation, they get a handle on the structures that the mind generates to interpret incoming data, and they can begin to build computational models of the process.

In Vision in Art and Neuroscience, we focus on the experiential as well as the experimental, probing the perceiver’s experience of structure-generating process—perceiving perception itself.

As instructors, we face the pedagogical question: what exercises, in the studio, can evoke so striking an experience of students’ own perception that cutting edge research takes on new meaning, understood in the immediacy of seeing? Later in the semester, students face a similar question as artists: How can one create visual environments where viewers experience their own perceptual processing at work? Done well, this experience becomes the artwork itself. Early in the course, students explore the Ganzfeld effect, popularized by artist James Turrell, where the viewer is exposed to an unstructured visual field of uniform illumination. In this experience, one feels the mind struggling to fit models of the world to unstructured input, and attempting this over and over again — an interpretation process which often goes unnoticed when input structure is expected by visual processing architecture. The progression of the course modules follows the hierarchy of visual processing in the brain, which builds increasingly complex interpretations of visual inputs, from brightness and edges to depth, color, and recognizable form.

MIT students first encounter those concepts in the seminar component of the course at the beginning of each week. Later in the week, students translate findings into experimental approaches in the studio. We work with light directly, from introducing a single pinpoint of light into an otherwise completely dark room, to building intricate environments using programmable electronics. Students begin to take this work into their own hands, in small groups and individually, culminating in final projects for exhibition. These exhibitions are truly a highlight of the course. They’re often one of the first times that students have built and shown artworks. That’s been a gift to share with the broader MIT community, and a great learning experience for students and instructors alike.

Q: How has that approach been received by the MIT community?

A: What we’re doing has resonated across disciplines: In addition to neuroscience, we have students and researchers joining us from computer science, mechanical engineering, mathematics, the Media Lab, and ACT (the Program in Art, Culture, and Technology). The course is growing into something larger, a community of practice interested in applying the scientific methodology we develop to study the world, to probe experience, and to articulate models for its generation and replication.

With a mix of undergraduates, graduates, faculty, and artists, we’ve put together installations and symposia — including three on campus so far. The first of these, “Perceiving Perception,” also led to a weekly open studio night where students and collaborators convene for project work. Our second exhibition, “Dessert of the Real,” is on display this spring in the Compton Gallery. This April we’re organizing a symposium in the studio featuring neuroscientists, computer scientists, artists and researchers from MIT and Harvard. We’re reaching beyond campus as well, through off-site installations, collaborations with museums — including the Metropolitan Museum of Art and the Peabody Essex Museum — and a partnership with the ZERO Group in Germany.

We’re eager to involve a broad network of collaborators. It’s an exciting moment in the fields of neuroscience and computing; there is great energy to build technologies that perceive the world like humans do. We stress on the first day of class that perception is a fundamentally creative act. We see the potential for models of perception to themselves be tools for scaling and translating creativity across domains, and for building a deeply creative relationship to our environment.

Elephant or chair? How the brain IDs objects

by Anne Trafton |

As visual information flows into the brain through the retina, the visual cortex transforms the sensory input into coherent perceptions. Neuroscientists have long hypothesized that a part of the visual cortex called the inferotemporal (IT) cortex is necessary for the key task of recognizing individual objects, but the evidence has been inconclusive.

In a new study, MIT neuroscientists have found clear evidence that the IT cortex is indeed required for object recognition; they also found that subsets of this region are responsible for distinguishing different objects.

In addition, the researchers have developed computational models that describe how these neurons transform visual input into a mental representation of an object. They hope such models will eventually help guide the development of brain-machine interfaces (BMIs) that could be used for applications such as generating images in the mind of a blind person.

“We don’t know if that will be possible yet, but this is a step on the pathway toward those kinds of applications that we’re thinking about,” says James DiCarlo, the head of MIT’s Department of Brain and Cognitive Sciences, a member of the McGovern Institute for Brain Research, and the senior author of the new study.

Rishi Rajalingham, a postdoc at the McGovern Institute, is the lead author of the paper, which appears in the March 13 issue of Neuron.

Distinguishing objects

In addition to its hypothesized role in object recognition, the IT cortex also contains “patches” of neurons that respond preferentially to faces. Beginning in the 1960s, neuroscientists discovered that damage to the IT cortex could produce impairments in recognizing non-face objects, but it has been difficult to determine precisely how important the IT cortex is for this task.

The MIT team set out to find more definitive evidence for the IT cortex’s role in object recognition, by selectively shutting off neural activity in very small areas of the cortex and then measuring how the disruption affected an object discrimination task. In animals that had been trained to distinguish between objects such as elephants, bears, and chairs, they used a drug called muscimol to temporarily turn off subregions about 2 millimeters in diameter. Each of these subregions represents about 5 percent of the entire IT cortex.

These experiments, which represent the first time that researchers have been able to silence such small regions of IT cortex while measuring behavior over many object discriminations, revealed that the IT cortex is not only necessary for distinguishing between objects, but it is also divided into areas that handle different elements of object recognition.

The researchers found that silencing each of these tiny patches produced distinctive impairments in the animals’ ability to distinguish between certain objects. For example, one subregion might be involved in distinguishing chairs from cars, but not chairs from dogs. Each region was involved in 25 to 30 percent of the tasks that the researchers tested, and regions that were closer to each other tended to have more overlap between their functions, while regions far away from each other had little overlap.

“We might have thought of it as a sea of neurons that are completely mixed together, except for these islands of “face patches.” But what we’re finding, which many other studies had pointed to, is that there is large-scale organization over the entire region,” Rajalingham says.

The features that each of these regions are responding to are difficult to classify, the researchers say. The regions are not specific to objects such as dogs, nor easy-to-describe visual features such as curved lines.

“It would be incorrect to say that because we observed a deficit in distinguishing cars when a certain neuron was inhibited, this is a ‘car neuron,’” Rajalingham says. “Instead, the cell is responding to a feature that we can’t explain that is useful for car discriminations. There has been work in this lab and others that suggests that the neurons are responding to complicated nonlinear features of the input image. You can’t say it’s a curve, or a straight line, or a face, but it’s a visual feature that is especially helpful in supporting that particular task.”

Bevil Conway, a principal investigator at the National Eye Institute, says the new study makes significant progress toward answering the critical question of how neural activity in the IT cortex produces behavior.

“The paper makes a major step in advancing our understanding of this connection, by showing that blocking activity in different small local regions of IT has a different selective deficit on visual discrimination. This work advances our knowledge not only of the causal link between neural activity and behavior but also of the functional organization of IT: How this bit of brain is laid out,” says Conway, who was not involved in the research.

Brain-machine interface

The experimental results were consistent with computational models that DiCarlo, Rajalingham, and others in their lab have created to try to explain how IT cortex neuron activity produces specific behaviors.

“That is interesting not only because it says the models are good, but because it implies that we could intervene with these neurons and turn them on and off,” DiCarlo says. “With better tools, we could have very large perceptual effects and do real BMI in this space.”

The researchers plan to continue refining their models, incorporating new experimental data from even smaller populations of neurons, in hopes of developing ways to generate visual perception in a person’s brain by activating a specific sequence of neuronal activity. Technology to deliver this kind of input to a person’s brain could lead to new strategies to help blind people see certain objects.

“This is a step in that direction,” DiCarlo says. “It’s still a dream, but that dream someday will be supported by the models that are built up by this kind of work.”

The research was funded by the National Eye Institute, the Office of Naval Research, and the Simons Foundation.

Ila Fiete joins the McGovern Institute

by Sabbi Lall |

Ila Fiete, an associate professor in the Department of Brain and Cognitive Sciences at MIT recently joined the McGovern Institute as an associate investigator. Fiete is working to understand the circuits that underlie short-term memory, integration, and inference in the brain.

Think about the simple act of visiting a new town and getting to know its layout as you explore it. What places are reachable from others? Where are landmarks relative to each other? Where are you relative to these landmarks? How do you get from here to where you want to go next?

The process that occurs as your brain tries to transform the few routes you follow into a coherent map of the world is just one of myriad examples of hard computations that the brain is constantly performing. Fiete’s goal is to understand how the brain is able to carry out such computations, and she is developing and using multiple tools to this end. These approaches include pure theoretical approaches to examine neural codes, building numerical dynamical models of circuit operation, and techniques to extract information about the underlying circuit dynamics from neural data.

Spatial navigation is a particularly interesting nut to crack from a neural perspective: The mapping devices on your phone have access to global satellite data, previously constructed detailed maps of the town, various additional sensors, and excellent non-leaky memory. By contrast, the brain must build maps, plan routes, and determine goals all using noisy, local sensors, no externally provided maps, and with noisy, forgetful or leaky neurons. Fiete is particularly interested in elucidating how the brain deals with noisy and ambiguous cues from the world to arrive at robust estimates about the world that resolve ambiguity. She is also interested in how the networks that are important for memory and integration develop through plasticity, learning, and development in the brain.

Fiete earned a BS in mathematics and physics at the University of Michigan then obtained her PhD in 2004 at Harvard University in the Department of Physics. She held a postdoctoral appointment at the Kavli Institute for Theoretical Physics at the University of California, Santa Barbara from 2004 to 2006, while she was also a visiting member of the Center for Theoretical Biophysics at the University of California at San Diego. Fiete subsequently spent two years at Caltech as a Broad Fellow in brain circuitry, and in 2008 joined the faculty of the University of Texas at Austin. She is currently an HHMI faculty scholar.

Peering under the hood of fake-news detectors

by Rob Matheson |

New work from researchers at the McGovern Institute for Brain Research at MIT peers under the hood of an automated fake-news detection system, revealing how machine-learning models catch subtle but consistent differences in the language of factual and false stories. The research also underscores how fake-news detectors should undergo more rigorous testing to be effective for real-world applications.

Popularized as a concept in the United States during the 2016 presidential election, fake news is a form of propaganda created to mislead readers, in order to generate views on websites or steer public opinion.

Almost as quickly as the issue became mainstream, researchers began developing automated fake news detectors — so-called neural networks that “learn” from scores of data to recognize linguistic cues indicative of false articles. Given new articles to assess, these networks can, with fairly high accuracy, separate fact from fiction, in controlled settings.

One issue, however, is the “black box” problem — meaning there’s no telling what linguistic patterns the networks analyze during training. They’re also trained and tested on the same topics, which may limit their potential to generalize to new topics, a necessity for analyzing news across the internet.

In a paper presented at the Conference and Workshop on Neural Information Processing Systems, the researchers tackle both of those issues. They developed a deep-learning model that learns to detect language patterns of fake and real news. Part of their work “cracks open” the black box to find the words and phrases the model captures to make its predictions.

Additionally, they tested their model on a novel topic it didn’t see in training. This approach classifies individual articles based solely on language patterns, which more closely represents a real-world application for news readers. Traditional fake news detectors classify articles based on text combined with source information, such as a Wikipedia page or website.

“In our case, we wanted to understand what was the decision-process of the classifier based only on language, as this can provide insights on what is the language of fake news,” says co-author Xavier Boix, a postdoc in the lab of Eugene McDermott Professor Tomaso Poggio at the Center for Brains, Minds, and Machines (CBMM), a National Science Foundation-funded center housed within the McGovern Institute.

“A key issue with machine learning and artificial intelligence is that you get an answer and don’t know why you got that answer,” says graduate student and first author Nicole O’Brien ’17. “Showing these inner workings takes a first step toward understanding the reliability of deep-learning fake-news detectors.”

The model identifies sets of words that tend to appear more frequently in either real or fake news — some perhaps obvious, others much less so. The findings, the researchers say, points to subtle yet consistent differences in fake news — which favors exaggerations and superlatives — and real news, which leans more toward conservative word choices.

“Fake news is a threat for democracy,” Boix says. “In our lab, our objective isn’t just to push science forward, but also to use technologies to help society. … It would be powerful to have tools for users or companies that could provide an assessment of whether news is fake or not.”

The paper’s other co-authors are Sophia Latessa, an undergraduate student in CBMM; and Georgios Evangelopoulos, a researcher in CBMM, the McGovern Institute of Brain Research, and the Laboratory for Computational and Statistical Learning.

Limiting bias

The researchers’ model is a convolutional neural network that trains on a dataset of fake news and real news. For training and testing, the researchers used a popular fake news research dataset, called Kaggle, which contains around 12,000 fake news sample articles from 244 different websites. They also compiled a dataset of real news samples, using more than 2,000 from the New York Times and more than 9,000 from The Guardian.

In training, the model captures the language of an article as “word embeddings,” where words are represented as vectors — basically, arrays of numbers — with words of similar semantic meanings clustered closer together. In doing so, it captures triplets of words as patterns that provide some context — such as, say, a negative comment about a political party. Given a new article, the model scans the text for similar patterns and sends them over a series of layers. A final output layer determines the probability of each pattern: real or fake.

The researchers first trained and tested the model in the traditional way, using the same topics. But they thought this might create an inherent bias in the model, since certain topics are more often the subject of fake or real news. For example, fake news stories are generally more likely to include the words “Trump” and “Clinton.”

“But that’s not what we wanted,” O’Brien says. “That just shows topics that are strongly weighting in fake and real news. … We wanted to find the actual patterns in language that are indicative of those.”

Next, the researchers trained the model on all topics without any mention of the word “Trump,” and tested the model only on samples that had been set aside from the training data and that did contain the word “Trump.” While the traditional approach reached 93-percent accuracy, the second approach reached 87-percent accuracy. This accuracy gap, the researchers say, highlights the importance of using topics held out from the training process, to ensure the model can generalize what it has learned to new topics.

More research needed

To open the black box, the researchers then retraced their steps. Each time the model makes a prediction about a word triplet, a certain part of the model activates, depending on if the triplet is more likely from a real or fake news story. The researchers designed a method to retrace each prediction back to its designated part and then find the exact words that made it activate.

More research is needed to determine how useful this information is to readers, Boix says. In the future, the model could potentially be combined with, say, automated fact-checkers and other tools to give readers an edge in combating misinformation. After some refining, the model could also be the basis of a browser extension or app that alerts readers to potential fake news language.

“If I just give you an article, and highlight those patterns in the article as you’re reading, you could assess if the article is more or less fake,” he says. “It would be kind of like a warning to say, ‘Hey, maybe there is something strange here.’”

Joining the dots in large neural datasets

by Sabbi Lall |

You might have played ‘join the dots’, a puzzle where numbers guide you to draw until a complete picture emerges. But imagine a complex underlying image with no numbers to guide the sequence of joining. This is a problem that challenges scientists who work with large amounts of neural data. Sometimes they can align data to a stereotyped behavior, and thus define a sequence of neuronal activity underlying navigation of a maze or singing of a song learned and repeated across generations of birds. But most natural behavior is not stereotyped, and when it comes to sleeping, imagining, and other higher order activities, there is not even a physical behavioral readout for alignment. Michale Fee and colleagues have now developed an algorithm, seqNMF, that can recognize relevant sequences of neural activity, even when there is no guide to align to, such as an overt sequence of behaviors or notes.

“This method allows you to extract structure from the internal life of the brain without being forced to make reference to inputs or output,” says Michale Fee, a neuroscientist at the McGovern Institute at MIT, Associate Department Head and Glen V. and Phyllis F. Dorflinger Professor of Neuroscience in the Department of Brain and Cognitive Sciences, and investigator with the Simons Collaboration on the Global Brain. Fee conducted the study in collaboration with Mark S. Goldman of the University of California, Davis.

In order to achieve this task, the authors of the study, co-led by Emily L. Mackevicius and Andrew H. Bahle of the McGovern Institute,  took a process called convolutional non-negative matrix factorization (NMF), a tool that allows extraction of sparse, but important, features from complex and noisy data, and developed it so that it can be used to extract sequences over time that are related to a learned behavior or song. The new algorithm also relies on repetition, but tell-tale repetitions of neural activity rather than simplistic repetitions in the animal’s behavior. seqNMF can follow repeated sequences of firing over time that are not tied to a specific external reference time framework, and can extract relevant sequences of neural firing in an unsupervised fashion without the researcher supplying prior information.

In the current study, the authors initially applied and honed the system on synthetic datasets. These datasets started to show them that the algorithm could “join the dots” without additional informational input. When seqNMF performed well in these tests, they applied it to available open source data from rats, finding that they could extract sequences of neural firing in the hippocampus that are relevant to finding a water reward in a maze.

Having passed these initial tests, the authors upped the ante and challenged seqNMF to find relevant neural activity sequences in a non-stereotypical behavior: improvised singing by zebra finches that have not learned the signature songs of their species (untutored birds). The authors analyzed neural data from the HVC, a region of the bird brain previously linked to song learning. Since normal adult bird songs are stereotyped, the researchers could align neural activity with features in the song itself for well-tutored birds. Fee and colleagues then turned to untutored birds and found that they still had repeated neural sequences related to the “improvised” song, that are reminiscent of the tutored birds, but the patterns are messier. Indeed, the brain of the untutored bird will even initiate two distinct neural signatures at the same time, but seqNMF is able to see past the resulting neural cacophony, and decipher that multiple patterns are present but overlapping. Being able to find these levels of order in such neural datasets is near impossible using previous methods of analysis.

seqNMF can be applied, potentially, to any neural activity, and the researchers are now testing whether the algorithm can indeed be generalized to extract information from other types of neural data. In other words, now that it’s clear that seqNMF can find a relevant sequence of neural activity for a non-stereotypical behavior, scientists can examine whether the neural basis of behaviors in other organisms and even for activities such as sleep and imagination can be extracted. Indeed, seqNMF is available on GitHub for researchers to apply to their own questions of interest.

Ila Fiete

Neural Coding and Dynamics

Ila Fiete builds tools and mathematical models to expand our knowledge of the brain’s computations. Specifically, her lab focuses on how the brain develops and reshapes its neural connections to perform high-level computations, like those involved in memory and learning. The Fiete lab applies cutting-edge theoretical and quantitative methods—wielding the vast capabilities of computational models, informed by mathematics, machine learning, and physics—digging deeper into how the brain represents and manipulates information. Through these strategies, Fiete hopes to shed new light onto the neural ensembles behind learning, integration of new information, inference-making, and spatial navigation.

Her lab’s findings are pushing the frontiers of neuroscience—while advancing the utility of computational tools in this space—and are building a more robust understanding of complex brain processes.

Josh McDermott

The Science of Hearing

Hearing enables us to make sense of our whereabouts, understand the emotional state of others, and enjoy musical experiences. Acoustic information relays vital cues about the world—yet much of the sophisticated brain system that decodes this information is poorly understood.

Josh McDermott’s research is in search of foundational principles of sound perception. Groundbreaking discoveries from the McDermott lab have clarified how people hear and process sounds. His research informs new treatments for those with hearing loss, and paves the way for machine systems that emulate the human ability to recognize and interpret sound. McDermott’s lab has also pioneered new approaches for understanding music perception. His lab deconstructs the neural ensembles that allow us to appreciate music, while also studying the often striking variation that can occur across cultures.

Virtual Tour of McDermott Lab

Rebecca Saxe

Mind Reading

How do we think about the thoughts of other people? How are some thoughts universal and others specific to a culture or an individual?

Rebecca Saxe is tackling these and other thorny questions surrounding human thought in adults, children, and infants. Leveraging behavioral testing, brain imaging, and computational modeling, her lab is focusing on a diverse set of research questions including what people learn from punishment, the role of generosity in social relationships, and the navigation and language abilities in toddlers. The team is also using computational models to deconstruct complex thought processes, such as how humans predict the emotions of others. This research not only expands the junction of sociology and neuroscience, but also unravels—and gives clarity to—the social threads that form the fabric of society.

Virtual Tour of Saxe Lab

Mehrdad Jazayeri

Neurobiology of Mental Computations

How does the brain give rise to the mind? How do neurons, circuits, and synapses in the brain encode knowledge about objects, events, and other structural and causal relationships in the environment? Research in Mehrdad Jazayeri’s lab brings together ideas from cognitive science, neuroscience, and machine learning with experimental data in humans, animals, and computer models to develop a computational understanding of how the brain create internal representations, or models, of the external world.